Integrating a model predictive control into a spray dryer simulator for a closed-loop control strategy

Abstract

Despite the wide-spread applications of spray drying in the production of chemicals, food, and pharmaceuticals, accurate control and development of this unit remains an elusive task because of the complex interactions of variables and phenomena. In this paper, we offer the development of a computationally efficient closed-loop system with a model predictive controller (MPC) where a computational fluid dynamics (CFD) process model is utilized to represent the process behavior. In particular, we present the development of an MPC and its implementation within the CFD model of a lab-scale spray dryer. Dynamic model is created with an adopted CFD model based on a 2D axisymmetric dryer, and Reaction Engineering Approach (REA)- as the drying kinetic method. Then, an MPC control strategy is designed using a linear approximation of a data-driven dynamic model and implemented within the CFD simulation as a user-defined function (UDF). We demonstrate the application of the developed MPC within the CFD simulation to address highly cross-coupled effects among different components of the dryer and guarantee desired product quality measures even in the presence of various disturbances. The proposed MPC embedded CFD modeling tool can serve as an efficient control strategy for other thermo-fluid systems as well.